Advanced instrumentation and cellular imaging techniques using high-throughput 3D electron microscopy are driving a new revolution in the exploration of complex biological systems by providing near seamless views across multiple scales of resolution. These datasets provide the necessary breadth and depth to analyze multicellular, cellular, and subcelluar structure across large swathes of neural tissue. While these new imaging procedures are generating extremely large datasets of enormous value, the quantities are such that no single user or even laboratory team can possibly analyze the full content of their own imaging activities through traditional means. To address this challenge, we propose to further develop and refine a prototype hybrid system for high-throughput segmentation of large neuropil datasets that: 1) advances automatic algorithms for segmentation of cellular and sub-cellular structures using machine learning techniques; 2) couples these techniques to a scalable and flexible process or tool suite allowing multiple users to simultaneously review, edit and curate the results of these automatic approaches; and, 3) builds a knowledge base of training data guiding and improving automated processing. This system will allow project scientists to select areas of interest, execute automatic segmentation algorithms, and distribute workload, curate data, and deposit final results into the Cell Centered Database (Martone et al. 2008) via accessible web-interfaces.